A drop-in compatible service for the OpenAI beta Assistants API with support for persistent threads, files, assistants, messages, and more using AstraDB (DataStax's db as a service offering powered by Apache Cassandra and jvector).

Compatible with existing OpenAI apps via the OpenAI SDKs by changing a single line of code.

Simply create an Astra DB Vector database and replace:

client = OpenAI(
    api_key=OPENAI_API_KEY,
)

with:

client = OpenAI(
    base_url="https://open-assistant-ai.astra.datastax.com/v1", 
    api_key=OPENAI_API_KEY,
    default_headers={
        "astra-api-token": ASTRA_DB_TOKEN,
    }
)

Optionally if you have an existing astra db you can pass your db_id in a second header. Otherwise the system will create one on your behalf and name it assistant_api_db using your token. Note, this means that the first request will hang until your db is ready (could be a couple of minutes). This will only happen once.

client = OpenAI(
    base_url="https://open-assistant-ai.astra.datastax.com/v1", 
    api_key=OPENAI_API_KEY,
    default_headers={
        "astra-api-token": ASTRA_DB_TOKEN,
        "astra-db-id": ASTRA_DB_ID
    }
)

Now you're ready to create an assistant

assistant = client.beta.assistants.create(
  instructions="You are a personal math tutor. When asked a math question, write and run code to answer the question.",
  model="gpt-4-1106-preview",
  tools=[{"type": "retrieval"}]
)

By default, the service uses AstraDB as the database/vector store and OpenAI for embeddings and chat completion.

We now support many third party models for both embeddings and completion thanks to litellm. Pass the api key of your service using api-key and embedding-model headers.

client = OpenAI(
    base_url="https://open-assistant-ai.astra.datastax.com/v1", 
    api_key="NONE",
    default_headers={
        "astra-api-token": ASTRA_DB_TOKEN,
        "api-key": COHERE_API_KEY,
        "embedding-model": "cohere/embed-english-v3.0",
    }
)

Note: remember to also pass your third party model to the assistant on create:

assistant = client.beta.assistants.create(
    name="Math Tutor",
    instructions="You are a personal math tutor. Answer questions briefly, in a sentence or less.",
    model="cohere/command",
)

For AWS Bedrock you can pass additional custom headers:

client = OpenAI(
    base_url="https://open-assistant-ai.astra.datastax.com/v1", 
    api_key="NONE",
    default_headers={
        "astra-api-token": ASTRA_DB_TOKEN,
        "embedding-model": "amazon.titan-embed-text-v1",
        "LLM-PARAM-aws-access-key-id": BEDROCK_AWS_ACCESS_KEY_ID,
        "LLM-PARAM-aws-secret-access-key": BEDROCK_AWS_SECRET_ACCESS_KEY,
        "LLM-PARAM-aws-region-name": BEDROCK_AWS_REGION,
    }
)

and again, specify the custom model for the assistant.

assistant = client.beta.assistants.create(
    name="Math Tutor",
    instructions="You are a personal math tutor. Answer questions briefly, in a sentence or less.",
    model="meta.llama2-13b-chat-v1",
)

Additional examples including third party LLMs (bedrock, cohere, perplexity, etc.) can be found under tests/examples

To run the examples using poetry create a .env file in this directory with your secrets and run:

poetry install

and

poetry run python examples/basic.py

See our coverage report here

• Support for other embedding models and LLMs
• Pluggable RAG strategies
• Tools / function support
• Streaming support